# Carga de librerias
import pandas as pd
import sys
from sklearn.decomposition import FactorAnalysis, PCA
import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
# import seaborn as sns
from scipy import stats
from kmodes.kprototypes import KPrototypes
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score
from sklearn.metrics.pairwise import euclidean_distances

#pyhton version
print("Version de Python: ", sys.version)

Version de Python:  3.12.1 (v3.12.1:2305ca5144, Dec  7 2023, 17:23:38) [Clang 13.0.0 (clang-1300.0.29.30)]

xls = pd.ExcelFile("data.xlsx")
tabla1_raw = pd.read_excel(xls, 'base de datos 1 jugadores')
print(tabla1_raw.shape)
tabla1_raw.head()

(274, 16)

tabla1_raw.describe()

tabla2_raw = pd.read_excel(xls, 'base de datos 2 niños')
print(tabla2_raw.shape)
tabla2_raw.head()

(428, 13)

tabla2_raw.describe()

corr = tabla1_raw.corr()
plt.figure(figsize=(10,8), dpi =500)
sns.heatmap(corr,annot=True,fmt=".2f", linewidth=.5)
plt.show()

zscore_df  = tabla1_raw.apply(stats.zscore, axis=0)
thres = 3

print("Que variables tienen outilers?")
print(zscore_df.applymap(lambda x: x>thres).any())

Que variables tienen outilers?
ID                     False
SEXO                   False
EDAD                    True
CATEGORIA              False
grupo                  False
Categoria deportiva    False
Influenciapaterna      False
organización           False
Expectativasallogro    False
Miedoaloerrores        False
destre.Aperder         False
destre.Aganar          False
dstre.durantejuego     False
destre.Juegojusto      False
habilidadessociales    False
satisvida              False
dtype: bool

/var/folders/dq/pwk6csh94hnd65tz2zzcp2br0000gn/T/ipykernel_77892/18405655.py:5: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
  print(zscore_df.applymap(lambda x: x>thres).any())

print(zscore_df.loc[zscore_df["EDAD"]>3]["EDAD"])
tabla1_raw.loc[zscore_df["EDAD"]>3]

225    4.368670
254    3.138754
259    3.292494
269    3.138754
Name: EDAD, dtype: float64

corr = tabla2_raw.corr()
plt.figure(figsize=(10,8), dpi =500)
sns.heatmap(corr,annot=True,fmt=".2f", linewidth=.5)
plt.show()

zscore_df  = tabla2_raw.apply(stats.zscore, axis=0)
thres = 3

print("Que variables tienen outilers?")
print(zscore_df.applymap(lambda x: x>thres).any())

Que variables tienen outilers?
id                     False
SEXO                   False
EDAD                   False
Autovaloración         False
Autoexperiencia        False
PrexiónExterna          True
ExperienciaPersonal    False
Compañeros             False
Profesor               False
Positivos              False
Negativos              False
IMC                     True
grupo                  False
dtype: bool

/var/folders/dq/pwk6csh94hnd65tz2zzcp2br0000gn/T/ipykernel_77892/556520888.py:5: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.
  print(zscore_df.applymap(lambda x: x>thres).any())

print(zscore_df.loc[zscore_df["PrexiónExterna"]>3]["PrexiónExterna"])
tabla2_raw.loc[zscore_df["PrexiónExterna"]>3]

123    3.148214
Name: PrexiónExterna, dtype: float64

print(zscore_df.loc[zscore_df["IMC"]>3]["IMC"])
tabla2_raw.loc[zscore_df["IMC"]>3]

215    3.157854
426    3.294293
427    3.508750
Name: IMC, dtype: float64

# Eliminamos outliers
tabla2_raw = tabla2_raw.loc[(zscore_df["IMC"]<3) & (zscore_df["PrexiónExterna"]<3)]

tabla1_raw.columns

Index(['ID', 'SEXO', 'EDAD', 'CATEGORIA', 'grupo', 'Categoria deportiva',
       'Influenciapaterna', 'organización', 'Expectativasallogro',
       'Miedoaloerrores', 'destre.Aperder', 'destre.Aganar',
       'dstre.durantejuego', 'destre.Juegojusto', 'habilidadessociales',
       'satisvida'],
      dtype='object')

columnas = ["Influenciapaterna", 	"organización", 	"Expectativasallogro",	"Miedoaloerrores",	"destre.Aperder",	"destre.Aganar", 	"dstre.durantejuego",	"destre.Juegojusto", 	"habilidadessociales",	"satisvida"]
tabla1 = tabla1_raw[columnas]

#Estandarizado 
st = StandardScaler()
tabla1_st = st.fit_transform(tabla1)
tabla1_st = pd.DataFrame(tabla1_st, columns=columnas)
tabla1_st

tabla1_sexo=tabla1_raw['SEXO']
tabla1_grupo=tabla1_raw['grupo']
tabla1_categoria=pd.get_dummies(tabla1_raw['Categoria deportiva'], drop_first=True, dtype=int)

columnas = ["Autovaloración", "Autoexperiencia","PrexiónExterna","ExperienciaPersonal","Compañeros","Profesor","Positivos","Negativos"]
tabla2 = tabla2_raw[columnas]

#Estandarizado 
st = StandardScaler()
tabla2_st = st.fit_transform(tabla2)
tabla2_st = pd.DataFrame(tabla2_st, columns=columnas)
tabla2_st

tabla2_raw['SEXO']

0      1
1      1
2      1
3      1
4      1
      ..
421    2
422    2
423    2
424    2
425    2
Name: SEXO, Length: 424, dtype: int64

tabla2_sexo=tabla2_raw['SEXO'].replace([1,2], [1,0])
tabla2_grupo=tabla2_raw['grupo'].replace([1,2], [1,0])

from sklearn.metrics import silhouette_score
from sklearn.cluster import KMeans
import seaborn as sns

inertias = []
silouette = []

k_candidates = range(2, 10)

for k in k_candidates:
    k_means = KMeans(random_state=42, n_clusters=k)
    preds = k_means.fit_predict(tabla1_st)
    inertias.append(k_means.inertia_)
    silouette.append(silhouette_score(tabla1_st, preds))

fig, ax = plt.subplots(figsize=(9, 6))
sns.scatterplot(x=k_candidates, y = inertias, s=80, ax=ax)
#sns.scatterplot(x=[k_candidates[2]], y = [inertias[2]], color=sns_c[3], s=150, ax=ax)
sns.lineplot(x=k_candidates, y = inertias, alpha=0.5, ax=ax)
ax.set(title='Inertia K-Means', ylabel='inertia', xlabel='k')

[Text(0.5, 1.0, 'Inertia K-Means'), Text(0, 0.5, 'inertia'), Text(0.5, 0, 'k')]

fig, ax = plt.subplots(figsize=(9, 6))
sns.scatterplot(x=k_candidates, y = silouette, s=80, ax=ax)
#sns.scatterplot(x=[k_candidates[2]], y = [inertias[2]], color=sns_c[3], s=150, ax=ax)
sns.lineplot(x=k_candidates, y = silouette, alpha=0.5, ax=ax)
ax.set(title='Silouette score K-Means', ylabel='Silouette score', xlabel='k')

[Text(0.5, 1.0, 'Silouette score K-Means'),
 Text(0, 0.5, 'Silouette score'),
 Text(0.5, 0, 'k')]

def calinski_harabasz_score(data, labels):
    """
    Calculate the Calinski-Harabasz index.
    """
    from sklearn.metrics import calinski_harabasz_score
    return calinski_harabasz_score(data, labels)

def maximum_diameter(data, labels):
    """
    Calculate the maximum diameter of clusters.
    """
    max_diameter = 0
    for label in np.unique(labels):
        cluster_points = data[labels == label]
        distances = euclidean_distances(cluster_points)
        max_cluster_diameter = np.max(distances)
        if max_cluster_diameter > max_diameter:
            max_diameter = max_cluster_diameter
    return max_diameter

def minimum_separation(data, labels):
    """
    Calculate the minimum separation between clusters.
    """
    min_separation = float('inf')
    for label1 in np.unique(labels):
        for label2 in np.unique(labels):
            if label1 != label2:
                cluster1_points = data[labels == label1]
                cluster2_points = data[labels == label2]
                distances = euclidean_distances(cluster1_points, cluster2_points)
                min_cluster_separation = np.min(distances)
                if min_cluster_separation < min_separation:
                    min_separation = min_cluster_separation
    return min_separation

def dunn_index(data, labels):
    """
    Calculate the Dunn index.
    """
    max_cluster_diameter = maximum_diameter(data, labels)
    min_intercluster_distance = minimum_separation(data, labels)
    return min_intercluster_distance / max_cluster_diameter

# Initialize lists to store metrics
inertias = []
silhouette = []
chs = []
max_diameters = []
min_separations = []
dunn_indices = []

k_candidates = range(2, 10)

for k in k_candidates:
    k_means = KMeans(random_state=42, n_clusters=k)
    preds = k_means.fit_predict(tabla1_st)
    
    # Inertia (within-cluster sum of squares)
    inertias.append(k_means.inertia_)
    
    # Silhouette Score
    silhouette.append(silhouette_score(tabla1_st, preds))
    
    # Calinski-Harabasz Index (CHI)
    chs.append(calinski_harabasz_score(tabla1_st, preds))
    
    # Maximum diameter
    max_diameters.append(maximum_diameter(tabla1_st, preds))
    
    # Minimum separation
    min_separations.append(minimum_separation(tabla1_st, preds))
    
    # Dunn index
    dunn_indices.append(dunn_index(tabla1_st, preds))

# Create a DataFrame to store the results
results_df = pd.DataFrame({
    'K': k_candidates,
    'Inertia': inertias,
    'Silhouette': silhouette,
    'Calinski-Harabasz': chs,
    'Max Diameter': max_diameters,
    'Min Separation': min_separations,
    'Dunn Index': dunn_indices
})

results_df

# BEST MODEL
kmeans_model = KMeans(n_clusters=3)
kmeans_labels = kmeans_model.fit_predict(tabla1_st)

df_categories = pd.concat([tabla1_st, tabla1_sexo, tabla1_grupo, tabla1_categoria], axis = 1)

#Choosing optimal K value
cost = []
for num_clusters in list(range(2,7)):
    kproto = KPrototypes(n_clusters=num_clusters, init='Huang', random_state=42,n_jobs=-2,max_iter=15,n_init=50) 
    kproto.fit_predict(df_categories, categorical=[10, 11, 12, 13])
    cost.append(kproto.cost_)

plt.plot(list(range(2,7)), cost)
plt.xlabel('K')
plt.ylabel('cost')
plt.show

<function matplotlib.pyplot.show(close=None, block=None)>

kproto = KPrototypes(n_clusters=3, init='Huang', random_state=42,n_jobs=-2,max_iter=15,n_init=50) 
kproto_labels = kproto.fit_predict(df_categories, categorical=[10, 11, 12, 13])

from sklearn.cluster import AgglomerativeClustering


silouette = []

k_candidates = range(2, 10)

for k in k_candidates:
    model = AgglomerativeClustering(compute_full_tree=True, n_clusters=k)
    preds = model.fit_predict(tabla1_st)
    silouette.append(silhouette_score(tabla1_st, preds))

fig, ax = plt.subplots(figsize=(9, 6))
sns.scatterplot(x=k_candidates, y = silouette, s=80, ax=ax)
#sns.scatterplot(x=[k_candidates[2]], y = [inertias[2]], color=sns_c[3], s=150, ax=ax)
sns.lineplot(x=k_candidates, y = silouette, alpha=0.5, ax=ax)
#ax.set(title='Silouette AGNES', silouette='Silouette', xlabel='k')

<Axes: >

import numpy as np
from matplotlib import pyplot as plt
from scipy.cluster.hierarchy import dendrogram

from sklearn.cluster import AgglomerativeClustering
from sklearn.datasets import load_iris


def plot_dendrogram(model, **kwargs):
    # Create linkage matrix and then plot the dendrogram

    # create the counts of samples under each node
    counts = np.zeros(model.children_.shape[0])
    n_samples = len(model.labels_)
    for i, merge in enumerate(model.children_):
        current_count = 0
        for child_idx in merge:
            if child_idx < n_samples:
                current_count += 1  # leaf node
            else:
                current_count += counts[child_idx - n_samples]
        counts[i] = current_count

    linkage_matrix = np.column_stack(
        [model.children_, model.distances_, counts]
    ).astype(float)

    # Plot the corresponding dendrogram
    dendrogram(linkage_matrix, **kwargs)



# setting distance_threshold=0 ensures we compute the full tree.
model = AgglomerativeClustering(distance_threshold=0, n_clusters=None)

model = model.fit(tabla1_st)
plt.title("Hierarchical Clustering Dendrogram")
# plot the top three levels of the dendrogram
plot_dendrogram(model, truncate_mode="level", p=5)
plt.xlabel("Number of points in node (or index of point if no parenthesis).")
plt.show()

agglo_model = AgglomerativeClustering(n_clusters=3)
agglo_labels = agglo_model.fit_predict(tabla1_st)

from sklearn.cluster import DBSCAN

# define the model
model = DBSCAN(eps=1.8, min_samples=5)
# fit model and predict clusters
dbscan_labels = model.fit_predict(tabla1_st)

set(dbscan_labels)

{-1, 0, 1, 2}

from sklearn.cluster import OPTICS

# define the model
model = OPTICS(eps=1.8, min_samples=5)
# fit model and predict clusters
optics_labels = model.fit_predict(tabla1_st)
# retrieve unique clusters
set(optics_labels)

{-1, 0, 1, 2, 3, 4, 5, 6}

from sklearn.cluster import Birch
# define the model
model = Birch(threshold=0.01, n_clusters=3)
# fit the model
birch_labels = model.fit_predict(tabla1_st)
# assign a cluster to each example
set(birch_labels)

{0, 1, 2}

pca =PCA(n_components=2)
tabla1_pca = pd.DataFrame(pca.fit_transform(tabla1_st), columns=["var1", "var2"])

pca.explained_variance_ratio_

array([0.2727657 , 0.23413556])

# Create a scatter plot
plt.figure(figsize=(12, 4))

# Plot for KMeans
plt.subplot(1, 6, 1)
plt.scatter(tabla1_pca["var1"], tabla1_pca["var2"], c=kmeans_labels, cmap='viridis')
plt.title('KMeans')

# Plot for Kproto
plt.subplot(1, 6, 2)
plt.scatter(tabla1_pca["var1"], tabla1_pca["var2"], c=kproto_labels, cmap='viridis')
plt.title('KPrototipos')

# Plot for Agglomerative Clustering
plt.subplot(1, 6, 3)
plt.scatter(tabla1_pca["var1"], tabla1_pca["var2"], c=agglo_labels, cmap='viridis')
plt.title('Agglomerative Clustering')

# Plot for BIRCH
plt.subplot(1, 6, 4)
plt.scatter(tabla1_pca["var1"], tabla1_pca["var2"], c=birch_labels, cmap='viridis')
plt.title('BIRCH')

# Plot for DBSCAN
plt.subplot(1, 6, 5)
plt.scatter(tabla1_pca["var1"], tabla1_pca["var2"], c=dbscan_labels, cmap='viridis')
plt.title('DBSCAN')

# Plot for OPTICS
plt.subplot(1, 6, 6)
plt.scatter(tabla1_pca["var1"], tabla1_pca["var2"], c=optics_labels, cmap='viridis')
plt.title('OPTICS')



plt.tight_layout()
plt.show()

# Create a scatter plot
plt.figure(figsize=(8, 8))

# Plot for KMeans
plt.subplot(1, 1, 1)
plt.scatter(tabla1_pca["var1"], tabla1_pca["var2"], c=kmeans_labels, cmap='viridis')
plt.title('KMeans')

Text(0.5, 1.0, 'KMeans')

from sklearn.metrics import silhouette_score
from sklearn.cluster import KMeans
import seaborn as sns

inertias = []
silouette = []

k_candidates = range(2, 10)

for k in k_candidates:
    k_means = KMeans(random_state=42, n_clusters=k)
    preds = k_means.fit_predict(tabla2_st)
    inertias.append(k_means.inertia_)
    silouette.append(silhouette_score(tabla2_st, preds))

fig, ax = plt.subplots(figsize=(9, 6))
sns.scatterplot(x=k_candidates, y = inertias, s=80, ax=ax)
#sns.scatterplot(x=[k_candidates[2]], y = [inertias[2]], color=sns_c[3], s=150, ax=ax)
sns.lineplot(x=k_candidates, y = inertias, alpha=0.5, ax=ax)
ax.set(title='Inertia K-Means', ylabel='inertia', xlabel='k')

[Text(0.5, 1.0, 'Inertia K-Means'), Text(0, 0.5, 'inertia'), Text(0.5, 0, 'k')]

fig, ax = plt.subplots(figsize=(9, 6))
sns.scatterplot(x=k_candidates, y = silouette, s=80, ax=ax)
#sns.scatterplot(x=[k_candidates[2]], y = [inertias[2]], color=sns_c[3], s=150, ax=ax)
sns.lineplot(x=k_candidates, y = silouette, alpha=0.5, ax=ax)
ax.set(title='Silouette score K-Means', ylabel='Silouette score', xlabel='k')

[Text(0.5, 1.0, 'Silouette score K-Means'),
 Text(0, 0.5, 'Silouette score'),
 Text(0.5, 0, 'k')]

# Initialize lists to store metrics
inertias = []
silhouette = []
chs = []
max_diameters = []
min_separations = []
dunn_indices = []

k_candidates = range(2, 10)

for k in k_candidates:
    k_means = KMeans(random_state=42, n_clusters=k)
    preds = k_means.fit_predict(tabla2_st)
    
    # Inertia (within-cluster sum of squares)
    inertias.append(k_means.inertia_)
    
    # Silhouette Score
    silhouette.append(silhouette_score(tabla2_st, preds))
    
    # Calinski-Harabasz Index (CHI)
    chs.append(calinski_harabasz_score(tabla2_st, preds))
    
    # Maximum diameter
    max_diameters.append(maximum_diameter(tabla2_st, preds))
    
    # Minimum separation
    min_separations.append(minimum_separation(tabla2_st, preds))
    
    # Dunn index
    dunn_indices.append(dunn_index(tabla2_st, preds))

# Create a DataFrame to store the results
results_df = pd.DataFrame({
    'K': k_candidates,
    'Inertia': inertias,
    'Silhouette': silhouette,
    'Calinski-Harabasz': chs,
    'Max Diameter': max_diameters,
    'Min Separation': min_separations,
    'Dunn Index': dunn_indices
})

results_df

# BEST MODEL
kmeans_model = KMeans(n_clusters=3)
kmeans_labels = kmeans_model.fit_predict(tabla2_st)

df_categories = pd.concat([tabla2_st.reset_index(drop=True), tabla2_sexo.reset_index(drop=True), tabla2_grupo.reset_index(drop=True)], axis = 1, ignore_index=True   )

#Choosing optimal K value
cost = []
for num_clusters in list(range(2,7)):
    kproto = KPrototypes(n_clusters=num_clusters, init='Huang', random_state=42,n_jobs=-2,max_iter=15,n_init=50) 
    kproto.fit_predict(df_categories, categorical=[8, 9])
    cost.append(kproto.cost_)

fig, ax = plt.subplots(figsize=(9, 6))
sns.scatterplot(x=list(range(2,7)), y = cost, s=80, ax=ax)
#sns.scatterplot(x=[k_candidates[2]], y = [inertias[2]], color=sns_c[3], s=150, ax=ax)
sns.lineplot(x=list(range(2,7)), y = cost, alpha=0.5, ax=ax)
ax.set(title='Inertia K-Means', ylabel='inertia', xlabel='k')

[Text(0.5, 1.0, 'Inertia K-Means'), Text(0, 0.5, 'inertia'), Text(0.5, 0, 'k')]

kproto = KPrototypes(n_clusters=3, init='Huang', random_state=42,n_jobs=-2,max_iter=15,n_init=50) 
kproto_labels = kproto.fit_predict(df_categories, categorical=[8, 9])

from sklearn.cluster import AgglomerativeClustering


silouette = []

k_candidates = range(2, 10)

for k in k_candidates:
    model = AgglomerativeClustering(compute_full_tree=True, n_clusters=k)
    preds = model.fit_predict(tabla2_st)
    silouette.append(silhouette_score(tabla2_st, preds))

fig, ax = plt.subplots(figsize=(9, 6))
sns.scatterplot(x=k_candidates, y = silouette, s=80, ax=ax).set_title('Silouette')
#sns.scatterplot(x=[k_candidates[2]], y = [inertias[2]], color=sns_c[3], s=150, ax=ax)
sns.lineplot(x=k_candidates, y = silouette, alpha=0.5, ax=ax)
#sns.set(title='Silouette AGNES', silouette='Silouette', xlabel='k')

<Axes: title={'center': 'Silouette'}>

import numpy as np
from matplotlib import pyplot as plt
from scipy.cluster.hierarchy import dendrogram

from sklearn.cluster import AgglomerativeClustering
from sklearn.datasets import load_iris


def plot_dendrogram(model, **kwargs):
    # Create linkage matrix and then plot the dendrogram

    # create the counts of samples under each node
    counts = np.zeros(model.children_.shape[0])
    n_samples = len(model.labels_)
    for i, merge in enumerate(model.children_):
        current_count = 0
        for child_idx in merge:
            if child_idx < n_samples:
                current_count += 1  # leaf node
            else:
                current_count += counts[child_idx - n_samples]
        counts[i] = current_count

    linkage_matrix = np.column_stack(
        [model.children_, model.distances_, counts]
    ).astype(float)

    # Plot the corresponding dendrogram
    dendrogram(linkage_matrix, **kwargs)



# setting distance_threshold=0 ensures we compute the full tree.
model = AgglomerativeClustering(distance_threshold=0, n_clusters=None)

model = model.fit(tabla2_st)
plt.title("Hierarchical Clustering Dendrogram")
# plot the top three levels of the dendrogram
plot_dendrogram(model, truncate_mode="level", p=5)
plt.xlabel("Number of points in node (or index of point if no parenthesis).")
plt.show()

# Initialize lists to store metrics
inertias = []
silhouette = []
chs = []
max_diameters = []
min_separations = []
dunn_indices = []

k_candidates = range(2, 10)

for k in k_candidates:
    model = AgglomerativeClustering( n_clusters=k)

    preds = model.fit_predict(tabla2_st)
    
    # Inertia (within-cluster sum of squares)
    #inertias.append(k_means.inertia_)
    
    # Silhouette Score
    silhouette.append(silhouette_score(tabla2_st, preds))
    
    # Calinski-Harabasz Index (CHI)
    chs.append(calinski_harabasz_score(tabla2_st, preds))
    
    # Maximum diameter
    max_diameters.append(maximum_diameter(tabla2_st, preds))
    
    # Minimum separation
    min_separations.append(minimum_separation(tabla2_st, preds))
    
    # Dunn index
    dunn_indices.append(dunn_index(tabla2_st, preds))

# Create a DataFrame to store the results
results_df = pd.DataFrame({
    'K': k_candidates,
    'Silhouette': silhouette,
    'Calinski-Harabasz': chs,
    'Max Diameter': max_diameters,
    'Min Separation': min_separations,
    'Dunn Index': dunn_indices
})

results_df

agglo_model = AgglomerativeClustering(n_clusters=3)
agglo_labels = agglo_model.fit_predict(tabla2_st)

tabla2_st["cluster"] = agglo_labels

tabla2_st.to_excel("estudio_ninos_agnes_3cluster.xlsx")

from sklearn.cluster import DBSCAN

# define the model
model = DBSCAN(eps=1.2, min_samples=5)
# fit model and predict clusters
dbscan_labels = model.fit_predict(tabla2_st)

set(dbscan_labels)

{-1, 0, 1, 2, 3, 4, 5}

from sklearn.cluster import OPTICS

# define the model
model = OPTICS(eps=1.90, min_samples=5)
# fit model and predict clusters
optics_labels = model.fit_predict(tabla2_st)
# retrieve unique clusters
set(optics_labels)

{-1, 0, 1, 2}

from sklearn.cluster import Birch
# define the model
model = Birch(threshold=0.01, n_clusters=6)
# fit the model
birch_labels = model.fit_predict(tabla2_st)
# assign a cluster to each example
set(birch_labels)

{0, 1, 2, 3, 4, 5}

pca =PCA(n_components=2)
tabla2_pca = pd.DataFrame(pca.fit_transform(tabla2_st), columns=["var1", "var2"])

pca.explained_variance_ratio_

array([0.3179844 , 0.16725695])

# Create a scatter plot
plt.figure(figsize=(12, 4))

# Plot for KMeans
plt.subplot(1, 6, 1)
plt.scatter(tabla2_pca["var1"], tabla2_pca["var2"], c=kmeans_labels, cmap='viridis')
plt.title('KMeans')

# Plot for KMeans
plt.subplot(1, 6, 2)
plt.scatter(tabla2_pca["var1"], tabla2_pca["var2"], c=kproto_labels, cmap='viridis')
plt.title('Kproto')

# Plot for Agglomerative Clustering
plt.subplot(1, 6, 3)
plt.scatter(tabla2_pca["var1"], tabla2_pca["var2"], c=agglo_labels, cmap='viridis')
plt.title('Agglomerative Clustering')

# Plot for BIRCH
plt.subplot(1, 6, 4)
plt.scatter(tabla2_pca["var1"], tabla2_pca["var2"], c=birch_labels, cmap='viridis')
plt.title('BIRCH')

# Plot for DBSCAN
plt.subplot(1, 6, 5)
plt.scatter(tabla2_pca["var1"], tabla2_pca["var2"], c=dbscan_labels, cmap='viridis')
plt.title('DBSCAN')

# Plot for OPTICS
plt.subplot(1, 6, 6)
plt.scatter(tabla2_pca["var1"], tabla2_pca["var2"], c=optics_labels, cmap='viridis')
plt.title('OPTICS')



plt.tight_layout()
plt.show()

# Create a scatter plot
plt.figure(figsize=(8, 8))

# Plot for Agglomerative Clustering
plt.subplot(1, 1, 1)
plt.scatter(tabla2_pca["var1"], tabla2_pca["var2"], c=agglo_labels, cmap='viridis')
plt.title('Agglomerative Clustering')

Text(0.5, 1.0, 'Agglomerative Clustering')

# Create a scatter plot
plt.figure(figsize=(8, 8))

# Plot for Agglomerative Clustering
plt.subplot(1, 1, 1)
plt.scatter(tabla2_pca["var1"], tabla2_pca["var2"], c=kmeans_labels, cmap='viridis')
plt.title('KMEANS Clustering')

Text(0.5, 1.0, 'KMEANS Clustering')

	ID	SEXO	EDAD	CATEGORIA	grupo	Categoria deportiva	Influenciapaterna	organización	Expectativasallogro	Miedoaloerrores	destre.Aperder	destre.Aganar	dstre.durantejuego	destre.Juegojusto	habilidadessociales	satisvida
count	274.000000	274.000000	274.000000	274.000000	274.000000	274.000000	274.000000	274.000000	274.000000	274.000000	274.000000	274.000000	274.000000	274.000000	274.000000	274.000000
mean	151.540146	0.682482	17.583942	4.383212	0.562044	2.069343	22.142336	22.766423	27.138686	28.967153	20.423358	21.492701	22.054745	22.186131	53.412409	19.854015
std	86.023084	0.466363	6.516412	1.673717	0.497043	0.859514	7.850585	5.340786	6.475858	9.010922	2.812121	2.436359	2.287231	2.196824	4.450130	3.523942
min	1.000000	0.000000	8.000000	1.000000	0.000000	1.000000	9.000000	7.000000	9.000000	12.000000	13.000000	15.000000	16.000000	16.000000	41.000000	9.000000
25%	76.250000	0.000000	13.000000	3.000000	0.000000	1.000000	16.250000	19.000000	23.000000	22.000000	18.250000	20.000000	21.000000	21.000000	50.250000	18.000000
50%	156.000000	1.000000	15.000000	4.000000	1.000000	2.000000	20.000000	23.000000	27.000000	27.000000	21.000000	21.500000	23.000000	23.000000	54.000000	20.000000
75%	225.750000	1.000000	21.000000	6.000000	1.000000	3.000000	29.000000	27.000000	32.000000	35.750000	23.000000	24.000000	24.000000	24.000000	57.000000	23.000000
max	298.000000	1.000000	46.000000	6.000000	1.000000	3.000000	45.000000	30.000000	45.000000	54.000000	25.000000	25.000000	25.000000	25.000000	60.000000	25.000000

	id	SEXO	EDAD	Autovaloración	Autoexperiencia	PrexiónExterna	ExperienciaPersonal	Compañeros	Profesor	Positivos	Negativos	IMC	grupo
count	428.000000	428.000000	428.000000	428.000000	428.000000	428.000000	428.000000	428.000000	428.000000	428.000000	428.000000	428.000000	428.000000
mean	220.172897	1.478972	10.429907	21.119159	26.280374	14.102804	19.712617	8.831776	11.969626	24.144860	17.380841	18.418540	1.261682
std	127.251831	0.500142	0.836656	7.341921	5.871847	5.373514	5.220580	2.552442	3.367639	2.931305	3.836507	2.604957	0.440065
min	1.000000	1.000000	8.000000	9.000000	11.000000	8.000000	8.000000	3.000000	4.000000	15.000000	10.000000	14.020000	1.000000
25%	110.750000	1.000000	10.000000	15.000000	22.000000	10.000000	16.000000	7.000000	9.000000	22.000000	15.000000	16.539750	1.000000
50%	219.500000	1.000000	11.000000	20.000000	27.000000	13.000000	19.000000	9.000000	12.000000	24.000000	17.000000	17.816000	1.000000
75%	329.250000	2.000000	11.000000	26.000000	30.000000	18.000000	23.000000	11.000000	14.000000	26.000000	20.000000	20.408000	2.000000
max	440.000000	2.000000	12.000000	42.000000	40.000000	31.000000	35.000000	15.000000	20.000000	30.000000	28.000000	27.548000	2.000000

	Influenciapaterna	organización	Expectativasallogro	Miedoaloerrores	destre.Aperder	destre.Aganar	dstre.durantejuego	destre.Juegojusto	habilidadessociales	satisvida
0	-0.273388	-2.394738	-0.794966	-0.329887	-1.575840	0.208601	-0.461989	-0.084883	-0.768214	-1.664251
1	-0.273388	-2.394738	-0.794966	-0.329887	-1.575840	0.208601	-0.461989	-0.084883	-0.768214	-1.664251
2	1.002733	0.231395	-0.485562	0.448370	-0.150823	-0.613798	-0.461989	-0.996954	-0.993338	-1.095667
3	-0.656224	0.043815	-0.640264	0.893088	-0.507077	0.208601	-0.023979	0.827189	-0.768214	-1.095667
4	-0.783837	1.356881	-0.640264	-1.330502	-0.507077	-0.202598	0.414031	0.371153	-1.443585	-0.811374
...	...	...	...	...	...	...	...	...	...	...
269	-1.421897	1.356881	-1.723180	-1.775220	0.917940	1.442200	0.852041	0.827189	0.807652	1.462964
270	-0.911449	1.356881	-0.176157	0.003652	0.917940	1.031001	0.852041	1.283224	0.807652	1.462964
271	-0.145776	-0.706509	-0.949669	-0.663425	1.630448	1.442200	1.290051	-0.540918	-0.768214	1.462964
272	-1.294285	-0.894090	-1.259073	-1.552861	0.917940	1.031001	1.290051	-0.084883	0.807652	1.462964
273	-0.145776	1.356881	0.287949	0.226011	0.205431	1.442200	1.290051	1.283224	1.483023	1.462964

	Autovaloración	Autoexperiencia	PrexiónExterna	ExperienciaPersonal	Compañeros	Profesor	Positivos	Negativos
0	-1.240097	-1.068907	-0.951815	-0.709610	0.069561	0.009822	1.312591	-0.881423
1	-0.281106	0.128721	-0.200988	-0.324367	0.856064	-0.287649	0.971992	-0.099029
2	-0.144107	-0.042369	-0.388695	-0.516988	0.069561	-0.287649	0.290794	-0.359827
3	0.129890	-1.411086	0.737546	-1.094853	0.462812	0.902237	0.290794	-0.620625
4	-0.829101	0.470900	-0.576402	-0.131746	0.462812	1.794652	1.312591	-0.881423
...	...	...	...	...	...	...	...	...
419	-0.692102	-1.753265	-0.951815	-1.672717	-1.503445	-1.180064	0.631393	-0.359827
420	0.540887	0.641990	0.174426	-0.131746	0.069561	-1.775007	-0.731002	0.161769
421	0.951883	1.155259	0.737546	-0.516988	1.249315	-1.180064	-1.412200	0.683365
422	1.225880	0.470900	2.614615	1.216604	0.462812	0.604765	-0.731002	-0.359827
423	2.047873	1.839617	-0.013281	-1.480095	-0.323690	0.009822	-0.731002	-0.620625

	K	Inertia	Silhouette	Calinski-Harabasz	Max Diameter	Min Separation	Dunn Index
0	2	2250.620516	0.158850	59.144231	9.134731	1.312861	0.143722
1	3	1844.212620	0.186407	65.816267	7.532074	1.403673	0.186359
2	4	1701.755791	0.156806	54.909159	6.736316	1.263181	0.187518
3	5	1648.696827	0.110497	44.514029	6.736316	1.312861	0.194893
4	6	1561.948148	0.111001	40.426169	6.596665	1.312861	0.199019
5	7	1484.358649	0.111397	37.643221	6.870506	1.156093	0.168269
6	8	1438.028280	0.119315	34.404696	6.268904	1.193176	0.190333
7	9	1371.833908	0.118652	33.036435	6.870506	1.085318	0.157968

LECTURA DE DATOS¶

CALIDAD DE DATOS¶

Tabla 1: Jugadores¶

Tabla 2: Niños¶

PREPROCESAMIENTO¶

Tabla 1: Jugadores¶

Tabla 2: Niños¶

MODELADO¶

Tabla 1: Jugadores¶

KMEANS¶

KPrototypes¶

AGNES. Agglomerative clustering¶

DBSCAN. Cluster densidad¶

OPTICS¶

BIRCH¶

VISUALIZATION¶

TABLE 2¶

KMEANS¶

KPrototypes¶

AGNES. Agglomerative clustering¶

DBSCAN. Cluster densidad¶

OPTICS¶

BIRCH¶

VISUALIZATION¶

	ID	EDAD	CATEGORIA	Categoria deportiva	Influenciapaterna	organización	Expectativasallogro	Miedoaloerrores	destre.Aperder	destre.Aganar	dstre.durantejuego	destre.Juegojusto	habilidadessociales	satisvida
0	163	13	3	1	20	10	22	26	16	22	21	22	50	14
1	164	13	3	1	20	10	22	26	16	22	21	22	50	14
2	252	12	3	1	30	24	24	33	20	20	21	20	49	16
3	251	13	3	1	17	23	23	37	19	22	22	24	50	16
4	162	13	3	1	16	30	23	17	19	21	23	23	47	17

	id	SEXO	EDAD	Autovaloración	Autoexperiencia	PrexiónExterna	ExperienciaPersonal	Compañeros	Profesor	Positivos	Negativos	IMC	grupo
0	388	1	11	12	20	9	16	9	12	28	14	15.234	1
1	300	1	9	19	27	13	18	11	11	27	17	15.023	1
2	299	1	9	20	26	12	17	9	11	25	16	15.259	1
3	274	1	9	22	18	18	14	10	15	25	15	14.109	1
4	363	1	10	15	29	11	19	10	18	28	14	14.348	1

	ID	SEXO	EDAD	CATEGORIA	grupo	Categoria deportiva	Influenciapaterna	organización	Expectativasallogro	Miedoaloerrores	destre.Aperder	destre.Aganar	dstre.durantejuego	destre.Juegojusto	habilidadessociales	satisvida
225	126	1	46	6	1	3	31	15	27	35	25	20	20	19	49	20
254	70	1	38	6	0	3	13	30	27	19	19	25	24	21	58	22
259	264	1	39	6	1	3	29	30	31	35	23	23	23	25	57	23
269	65	1	38	6	0	3	11	30	16	13	23	25	24	24	57	25

	id	SEXO	EDAD	Autovaloración	Autoexperiencia	PrexiónExterna	ExperienciaPersonal	Compañeros	Profesor	Positivos	Negativos	IMC	grupo
215	381	1	11	31	20	11	15	8	13	27	21	26.635	2
426	123	2	12	30	34	14	23	6	12	23	21	26.990	2
427	111	2	11	16	27	14	20	11	7	22	15	27.548	2

	K	Inertia	Silhouette	Calinski-Harabasz	Max Diameter	Min Separation	Dunn Index
0	2	2711.024754	0.178248	106.001081	7.982400	0.713055	0.089328
1	3	2482.794139	0.124519	77.085664	7.982400	0.905189	0.113398
2	4	2288.604556	0.124512	67.497621	7.982400	0.896916	0.112362
3	5	2098.083806	0.134153	64.600718	6.968390	0.854630	0.122644
4	6	1992.267940	0.137366	58.735875	6.968390	0.829888	0.119093
5	7	1948.185750	0.117733	51.506942	6.896896	0.986900	0.143093
6	8	1838.950160	0.122352	50.189252	6.626053	0.657693	0.099259
7	9	1758.374420	0.121398	48.194700	6.777655	0.896916	0.132334

	K	Silhouette	Calinski-Harabasz	Max Diameter	Min Separation	Dunn Index
0	2	0.168166	75.481573	8.056772	1.268184	0.157406
1	3	0.090993	62.897170	7.810183	0.986900	0.126361
2	4	0.088696	53.289081	7.810183	0.986900	0.126361
3	5	0.087042	48.295763	7.618770	0.896916	0.117724
4	6	0.079233	45.561925	7.618770	0.896916	0.117724
5	7	0.090385	43.101921	7.240197	0.896916	0.123880
6	8	0.089571	40.973571	7.240197	0.896916	0.123880
7	9	0.091980	39.600285	7.240197	0.896916	0.123880

	ID	EDAD	CATEGORIA	Categoria deportiva	Influenciapaterna	organización	Expectativasallogro	Miedoaloerrores	destre.Aperder	destre.Aganar	dstre.durantejuego	destre.Juegojusto	habilidadessociales	satisvida
0	163	13	3	1	20	10	22	26	16	22	21	22	50	14
1	164	13	3	1	20	10	22	26	16	22	21	22	50	14
2	252	12	3	1	30	24	24	33	20	20	21	20	49	16
3	251	13	3	1	17	23	23	37	19	22	22	24	50	16
4	162	13	3	1	16	30	23	17	19	21	23	23	47	17

	ID	SEXO	EDAD	CATEGORIA	grupo	Categoria deportiva	Influenciapaterna	organización	Expectativasallogro	Miedoaloerrores	destre.Aperder	destre.Aganar	dstre.durantejuego	destre.Juegojusto	habilidadessociales	satisvida
225	126	1	46	6	1	3	31	15	27	35	25	20	20	19	49	20
254	70	1	38	6	0	3	13	30	27	19	19	25	24	21	58	22
259	264	1	39	6	1	3	29	30	31	35	23	23	23	25	57	23
269	65	1	38	6	0	3	11	30	16	13	23	25	24	24	57	25

	ID	EDAD	CATEGORIA	Categoria deportiva	Influenciapaterna	organización	Expectativasallogro	Miedoaloerrores	destre.Aperder	destre.Aganar	dstre.durantejuego	destre.Juegojusto	habilidadessociales	satisvida
0	163	13	3	1	20	10	22	26	16	22	21	22	50	14
1	164	13	3	1	20	10	22	26	16	22	21	22	50	14
2	252	12	3	1	30	24	24	33	20	20	21	20	49	16
3	251	13	3	1	17	23	23	37	19	22	22	24	50	16
4	162	13	3	1	16	30	23	17	19	21	23	23	47	17

	ID	SEXO	EDAD	CATEGORIA	grupo	Categoria deportiva	Influenciapaterna	organización	Expectativasallogro	Miedoaloerrores	destre.Aperder	destre.Aganar	dstre.durantejuego	destre.Juegojusto	habilidadessociales	satisvida
225	126	1	46	6	1	3	31	15	27	35	25	20	20	19	49	20
254	70	1	38	6	0	3	13	30	27	19	19	25	24	21	58	22
259	264	1	39	6	1	3	29	30	31	35	23	23	23	25	57	23
269	65	1	38	6	0	3	11	30	16	13	23	25	24	24	57	25